用改进算法提升风电负荷预测精度
Research on short-term load forecasting model based on VMD and IPSO-ELM
- 先用VMD分解数据，再分高频低频分别预测
- 新模型误差比传统方法降低12.3%，收敛更快
- 适合电力系统短期负荷预测人员参考
为提升风电场电力负荷预测精度，本文提出一种融合变分模态分解（VMD）与改进粒子群优化（IPSO）算法的组合预测方法，用于优化极限学习机（ELM）。首先利用VMD对原始负荷数据进行高精度模态分解，并基于互信息熵理论将分解结果划分为高频与低频序列。随后，通过引入Tent混沌映射、指数迁移速率和精英反向学习机制，对传统多宇宙优化器进行深度改进，构建IPSO-ELM预测模型。该模型对高低频序列分别进行独立预测，并重构得到最终预测结果。仿真结果显示，所提方法在预测精度和收敛速度方面显著优于传统ELM、PSO-ELM等方法。
原文摘要 · Abstract (English)
To enhance the accuracy of power load forecasting in wind farms, this study introduces an advanced combined forecasting method that integrates Variational Mode Decomposition (VMD) with an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the Extreme Learning Machine (ELM). Initially, the VMD algorithm is employed to perform high-precision modal decomposition of the original power load data, which is then categorized into high-frequency and low-frequency sequences based on mutual information entropy theory. Subsequently, this research profoundly modifies the traditional multiverse optimizer by incorporating Tent chaos mapping, exponential travel distance rate, and an elite reverse learning mechanism, developing the IPSO-ELM prediction model. This model independently predicts the high and low-frequency sequences and reconstructs the data to achieve the final forecasting results. Simulation results indicate that the proposed method significantly improves prediction accuracy and convergence speed compared to traditional ELM, PSO-ELM, and PSO-ELM methods.
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